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Scaling Up Structural Clustering to Large Probabilistic Graphs Using Lyapunov Central Limit Theorem

Summary: Recasts expected Jaccard similarity in probabilistic-graph structural clustering as a one-tailed Normal CDF via a Lyapunov CLT-based construction of random variables. Achieves linear runtime (vs prior quadratic DP), enabling web-scale clustering of massive graphs. (summarized by gpt-5-mini on Feb 09 2026)

Paper ID
13154
Venue
VLDB
Year
2023
Pagerank
4.1905499e-05
Overall Rank
11,261 | 21.74%
DOI
10.14778/3611479.3611516

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Citations counted here include only citations to other VLDB/SIGMOD/CIDR/PODS papers in this database.

Rank Cited Paper Year Venue Pagerank
1,159 k-Nearest Neighbors in Uncertain Graphs 2010 VLDB 0.00013584223
1,448 Distance-Constraint Reachability Computation in Uncertain Graphs 2011 VLDB 0.00011928845
3,106 Injecting Uncertainty in Graphs for Identity Obfuscation 2012 VLDB 7.5550946e-05
3,622 Efficient and Effective Algorithms for Clustering Uncertain Graphs 2019 VLDB 6.9021704e-05
4,654 Dynamic Structural Clustering on Graphs 2021 SIGMOD 6.0129362e-05
6,522 Clustering Uncertain Graphs 2018 VLDB 5.0230003e-05
7,903 Coarsening Massive Influence Networks for Scalable Diffusion Analysis 2017 SIGMOD 4.6170606e-05
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